There has been some discussion on the Numba mailing list as well about a version of guvectorize that doesn't compile for testing and flexibility.
Having this be inside NumPy itself seems ideal.
On Tue, Sep 13, 2016 at 12:59 PM, Stephan Hoyer firstname.lastname@example.org wrote:
On Tue, Sep 13, 2016 at 10:39 AM, Nathan Goldbaum email@example.com wrote:
I'm curious whether you have a plan to deal with the python functional call overhead. Numba gets around this by JIT-compiling python functions - is there something analogous you can do in NumPy or will this always be limited by the overhead of repeatedly calling a Python implementation of the "core" operation?
I don't think there is any way to avoid this in NumPy proper, but that's OK (it's similar to the existing overhead of vectorize).
Numba already has guvectorize (and it's own version of vectorize as well), which already does exactly this.
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